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1000 Functional Connectomes Project

A fully open downloadable database of 1200 resting state fMRI (R-fMRI) datasets collected from 33 sites around the world. The era of discovery science for human brain function was inaugurated by the collaborative launch of the Project by leading members of the functional magnetic resonance imaging (fMRI) community. Following the precedent of full unrestricted data sharing, which has become the norm in molecular genetics, the Functional Connectomes Project (FCP) entailed the aggregation and public release of over 1200 resting state fMRI (R-fMRI) datasets collected from 33 sites around the world. Having provided the first large-scale demonstration of the feasibility and scientific value of open sharing of R-fMRI data, the next major challenge is to make the aggregation and sharing of well-phenotyped datasets a cultural norm for the imaging community. Comprehensive phenotypic information must be made available with imaging datasets to facilitate sophisticated data-mining a process by which novel relationships between phenotypic and imaging data can be revealed. A second paradigm shift from retrospective to prospective data sharing is also necessary. That is, in contrast to the FCP release, which primarily comprised datasets that had already been published, prospective data sharing involves regularly scheduled (e.g., weekly, monthly, or quarterly) sharing of data collected at contributing sites, as the data is being collected. The notion of sharing newly acquired data, rather than waiting until those data have been published, is novel in the imaging community, but is common practice in fields such as genetics where discovery science has been successfully implemented. In order for such a shift in practice to occur, one or more imaging groups must take the lead, and set an example for the field. Objectives: # To enhance the 1000 FCP by including comprehensive phenotypic data. # To establish a common protocol for sharing phenotypic/metadata via the 1000 FCP. # To initiate open, prospective data-sharing for the neuroimaging community.

URL: http://fcon_1000.projects.nitrc.org/

Resource ID: nlx_144428     Resource Type: Resource     Version: Latest Version


resting state functional mri, fmri, brain, neuroimaging, phenotype, function, data sharing, human, mri, r-fmri, rs-fmri, fc-fmri, rs--fcmri, resting-state, dicom, dti, child, adolescent, brain imaging, neuroinformatics, adult human, phenotype, data set

Additional Resource Types

Data Repository, Image Collection, Image Repository, Catalog

Used By

NIF Data Federation

Alternate URLs




Related To

NITRC-IR, NITRC-IR, NIH Data Sharing Repositories


1000 FCP, FCP

Parent Organization

Publication Link



fcon_1000, 1000 Functional Connectomes Project INDI

Listed By

NITRC, NIH Data Sharing Repositories


Account required, The community can contribute to this resource, Creative Commons Attribution-NonCommercial License



Original Submitter


Version Status


Submitted On

12:00am April 18, 2012

Originated From


Changes from Previous Version

  • Synonyms was changed

Version 6

Created 1 week ago by Kristen Jensen

Version 5

Created 2 weeks ago by Christie Wang

Version 4

Created 3 weeks ago by Christie Wang

Version 3

Created 3 weeks ago by Christie Wang

Version 2

Created 2 months ago by Kristen Jensen

Version 1

Created 3 years ago by Anonymous

Fully exploratory network independent component analysis of the 1000 functional connectomes database.

  • Kalcher K
  • Front Hum Neurosci
  • 2012 7

The 1000 Functional Connectomes Project is a collection of resting-state fMRI datasets from more than 1000 subjects acquired in more than 30 independent studies from around the globe. This large, heterogeneous sample of resting-state data offers the unique opportunity to study the consistencies of resting-state networks at both subject and study level. In extension to the seminal paper by Biswal et al. (2010), where a repeated temporal concatenation group independent component analysis (ICA) approach on reduced subsets (using 20 as a pre-specified number of components) was used due to computational resource limitations, we herein apply Fully Exploratory Network ICA (FENICA) to 1000 single-subject independent component analyses. This, along with the possibility of using datasets of different lengths without truncation, enabled us to benefit from the full dataset available, thereby obtaining 16 networks consistent over the whole group of 1000 subjects. Furthermore, we demonstrated that the most consistent among these networks at both subject and study level matched networks most often reported in the literature, and found additional components emerging in prefrontal and parietal areas. Finally, we identified the influence of scan duration on the number of components as a source of heterogeneity between studies.

Making data sharing work: the FCP/INDI experience.

  • Mennes M
  • Neuroimage
  • 2013 15

Over a decade ago, the fMRI Data Center (fMRIDC) pioneered open-access data sharing in the task-based functional neuroimaging community. Well ahead of its time, the fMRIDC effort encountered logistical, sociocultural and funding barriers that impeded the field-wise instantiation of open-access data sharing. In 2009, ambitions for open-access data sharing were revived in the resting state functional MRI community in the form of two grassroots initiatives: the 1000 Functional Connectomes Project (FCP) and its successor, the International Neuroimaging Datasharing Initiative (INDI). Beyond providing open access to thousands of clinical and non-clinical imaging datasets, the FCP and INDI have demonstrated the feasibility of large-scale data aggregation for hypothesis generation and testing. Yet, the success of the FCP and INDI should not be confused with widespread embracement of open-access data sharing. Reminiscent of the challenges faced by fMRIDC, key controversies persist and include participant privacy, the role of informatics, and the logistical and cultural challenges of establishing an open science ethos. We discuss the FCP and INDI in the context of these challenges, highlighting the promise of current initiatives and suggesting solutions for possible pitfalls.